{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T18:06:57Z","timestamp":1772042817274,"version":"3.50.1"},"reference-count":43,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2021,3,12]],"date-time":"2021-03-12T00:00:00Z","timestamp":1615507200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41301514"],"award-info":[{"award-number":["41301514"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41401456"],"award-info":[{"award-number":["41401456"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science and Technology Project of Nantong","award":["MS12020112"],"award-info":[{"award-number":["MS12020112"]}]},{"name":"Nantong Key Laboratory Project","award":["CP12016005"],"award-info":[{"award-number":["CP12016005"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Spatial analysis is an important means of mining floating car trajectory information, and clustering method and density analysis are common methods among them. The choice of the clustering method affects the accuracy and time efficiency of the analysis results. Therefore, clarifying the principles and characteristics of each method is the primary prerequisite for problem solving. Taking four representative spatial analysis methods\u2014KMeans, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Clustering by Fast Search and Find of Density Peaks (CFSFDP), and Kernel Density Estimation (KDE)\u2014as examples, combined with the hotspot spatiotemporal mining problem of taxi trajectory, through quantitative analysis and experimental verification, it is found that DBSCAN and KDE algorithms have strong hotspot discovery capabilities, but the heat regions\u2019 shape of DBSCAN is found to be relatively more robust. DBSCAN and CFSFDP can achieve high spatial accuracy in calculating the entrance and exit position of a Point of Interest (POI). KDE and DBSCAN are more suitable for the classification of heat index. When the dataset scale is similar, KMeans has the highest operating efficiency, while CFSFDP and KDE are inferior. This paper resolves to a certain extent the lack of scientific basis for selecting spatial analysis methods in current research. The conclusions drawn in this paper can provide technical support and act as a reference for the selection of methods to solve the taxi trajectory mining problem.<\/jats:p>","DOI":"10.3390\/ijgi10030161","type":"journal-article","created":{"date-parts":[[2021,3,12]],"date-time":"2021-03-12T11:56:55Z","timestamp":1615550215000},"page":"161","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Applicability Evaluation of Several Spatial Clustering Methods in Spatiotemporal Data Mining of Floating Car Trajectory"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6953-501X","authenticated-orcid":false,"given":"Hao-xuan","family":"Chen","sequence":"first","affiliation":[{"name":"School of Geographical Sciences, Nantong University, Nantong 226007, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7317-5974","authenticated-orcid":false,"given":"Fei","family":"Tao","sequence":"additional","affiliation":[{"name":"School of Geographical Sciences, Nantong University, Nantong 226007, China"},{"name":"Department of Geography, University of Wisconsin-Madison, Madison, WI 53706, USA"},{"name":"Key Laboratory of Virtual Geographical Environment, MOE, Nanjing Normal University, Nanjing 210046, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2865-9515","authenticated-orcid":false,"given":"Pei-long","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Geographical Sciences, Nantong University, Nantong 226007, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7302-2583","authenticated-orcid":false,"given":"Li-na","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Geographical Sciences, Nantong University, Nantong 226007, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3041-6264","authenticated-orcid":false,"given":"Tong","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Geographical Sciences, Nantong University, Nantong 226007, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1080\/15230406.2015.1014424","article-title":"Inferring trip purposes and uncovering travel patterns from taxi trajectory data","volume":"43","author":"Gong","year":"2016","journal-title":"Cartogr. 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